scRNA-seq data was captured from the bone marrow mononuclear cells of two patients with leukemia before and after undergoing a hematopoietic stem cell transplant. We subjected the cells from each patient to various dimensionality reduction techniques to determine whether they are capable of distinguishing the treatment states of the cells. scRNA-seq data from two healthy controls are used to perform cPCA and scPCA.
The following table provides the gene symbol and loadings of the genes with non-zero entries in one of the loadings vectors of the first two scPCs.
## # A tibble: 5 x 3
## gene_sym scPC1 scPC2
## <chr> <dbl> <dbl>
## 1 CA1 0.0470 0
## 2 STMN1 -0.997 0
## 3 LDHA 0 0.482
## 4 C1QBP -0.0606 0
## 5 PDLIM1 0 -0.876
The absolute valules of the loadings are not compared for this patient, since only three entries in the leading loadings vectors produced by scPCA are non-zero.
5-fold cross-validation was used to tune the contrastive parameter.
5-fold cross-validation was used to tune the contrastive parameter.
The following table provides the gene symbol and loadings of the genes with non-zero entries in one of the loadings vectors of the first two scPCs.
## # A tibble: 117 x 3
## gene_sym scPC1 scPC2
## <chr> <dbl> <dbl>
## 1 HBB -0.0618 0.712
## 2 HBA2 0 -0.117
## 3 HBA1 0.104 -0.660
## 4 RPL13 -0.0151 0
## 5 RPL13A -0.0126 0
## 6 RPS4X -0.0710 0
## 7 PRDX2 0.0181 0
## 8 PTMA 0.0787 0
## 9 RPL19 -0.0687 0
## 10 FTH1 -0.00350 0
## # … with 107 more rows
5-fold cross-validation was used to tune the contrastive parameter.
5-fold cross-validation was used to tune the contrastive parameter.
The median running times over 5 repetitions of each method are presented below.